Yes, it is possible to apply AI-based sorting system to multiple types of crops. However, as the number and variations of defects in different crops increase, more data needs to be collected and the system needs to be fine-tuned for each crop. Additionally, additional modifications to the AI may be required, which can result in additional costs. During the data consultation process, it is important to clearly communicate the types of crops that you wish to apply the system to. Additional fees may be incurred if you wish to apply the system to multiple crops with different types of defects, while no additional fees may be incurred if you wish to apply the system to multiple varieties of the same crop. 

If the classification of crops by grade is clearly presented in the selection work area, it is possible to classify up to the desired area, regardless of how many steps are involved. However, in case of no clear classification, AIOFARM classifies it as discard/processing/distribution. Each item is done during the pre-consultation process.

Our data team will visit in advance to consult comprehensively about the types of defects in the desired raw materials and whether it is possible to apply to multiple crops, and determine whether the defects to be detected can be analyzed in our system, and clearly discuss how to classify them. If you have a purchase preference, please contact AIOFARM in advance for a smooth installation. It is better to contact in advance. 

Our system can distinguish all types of defects that can be visually distinguished. Even if similar-looking defects are caused by similar colors, our system can clearly analyze the defects to a level that can be distinguished by the naked eye of experts.

The difference between our inspection system and traditional systems is that traditional systems simply process the light spectrum reflected from the raw materials using computer equations, so in order to inspect various forms of defects, multiple sensors had to be used simultaneously. Our inspection system uses deep learning to analyze the characteristics of color images, allowing it to detect various forms of defects. As a result, compared to other inspection systems, our system has the advantages of 1) relatively lower cost, 2) the ability to simultaneously analyze all types of defects that can be visually distinguished, and 3) high accuracy of analysis.